A number of systems and programs are offered on the market for the design, the engineering and the manufacturing of objects. CAD is an acronym for Computer-Aided Design, e.g. it relates to software solutions for designing an object. CAE is an acronym for Computer-Aided Engineering, e.g. it relates to software solutions for simulating the physical behavior of a future product. CAM is an acronym for Computer-Aided Manufacturing, e.g. it relates to software solutions for defining manufacturing processes and operations. In such computer-aided design systems, the graphical user interface plays an important role as regards the efficiency of the technique. These techniques may be embedded within Product Lifecycle Management (PLM) systems. PLM refers to a business strategy that helps companies to share product data, apply common processes, and leverage corporate knowledge for the development of products from conception to the end of their life, across the concept of extended enterprise. The PLM solutions provided by Dassault Systènnes (under the trademarks CATIA, ENOVIA and DELMIA) provide an Engineering Hub, which organizes product engineering knowledge, a Manufacturing Hub, which manages manufacturing engineering knowledge, and an Enterprise Hub which enables enterprise integrations and connections into both the Engineering and Manufacturing Hubs. All together the system delivers an open object model linking products, processes, resources to enable dynamic, knowledge-based product creation and decision support that drives optimized product definition, manufacturing preparation, production and service.
A three-dimensional (3D) shape can be represented as surface based and volumetric. In surface based representations, 3D geometry is defined by a closed or open surface. The surface can be composed of triangles with vertices which are 3D points. The surface based representation is common in CAD/CAM and in computer information. In volumetric representation, the 3D shape is defined by a function ƒ(x, y, z) defined over the 3D space, either continuously or piecewise, by values stored in a voxel grid. The 3D-geometry is then further defined as the region in space which satisfies a certain value of the function. Generally, if ƒ(x, y, z) is scalar-valued, the shape is defined as ƒ(x, y, z)<s (or ƒ(x, y, z)>s) where s an appropriate threshold value. The volumetric representation is common in medical applications, in particular computerized tomography. As a special case, the region may be defined as being a narrow band between a lower and upper limit, in which case the definition might be s−w<ƒ(x, y, z)<s+w for a narrow band of width 2w centered around s.
Image segmentation separates zones of an image, e.g. a two-dimensional (2D) image or a 3D image such as a 3D virtual object. For example, in a 2D image of an object taken indoors, image segmentation can include identifying the part of the image representing the floor and isolating that part from other features of the image, such as furniture, walls, etc. In a 3D-virtual object representing a person, segmentation can include distinguishing clothing from bare skin, or distinguishing the arm from the torso.
Segmentation can be employed in many image analysis tasks. For example, for a traffic control system, a computer coupled to a roadside camera can employ segmentation to identify and count passing cars. Similarly, in a surveillance system, image segmentation can identify a human figure in an image and localize joints and limbs, which can facilitate identification of intruders. Reconstruction of 3D shapes from images can also employ segmentation, for example, when the reconstruction includes identifying semantically important parts of the object.
In most existing solutions, segmentation is performed on image data containing color (e.g., red-green-blue (RGB)) data and optionally depth data. Depth data represents, for each pixel, the distance from the sensor. Depth data can be captured using available devices such as the Microsoft Kinect®, Asus Xtion™ or Google Tango™.
In this context, there is still a need for improving computer vision, and notably image segmentation.